This is a decent course that covers an important topic that I've had a trouble finding good resources for learning about.

Pros: comprehensive coverage of the topic at a high level.

Cons: not enough examples to understand what is talked about in the lectures (especially the continuous data and prior with normal distribution lectures) and to anchor the topic in its practical uses.

I recommend supplementing this course with the MIT OCW 18-05 statistics class (I actually put this on hold and did that then came back).

If this course had a lot more practice problems with fully worked out answers it would help tremendously. I understand a sequel to this class is in the works and I look forward to taking it.

This course is PERFECT during weeks 1-3, failing only in the final week. From now on I'll focus my review on improvement points. I'd appreciate much more if the regression lesson was removed, and the normal distribution lesson received more attention. I understand that a more solid mathematical background is needed, but it could be done (much better than it is already done) in the written suplementary material. IMHO, the Bayesian methods for obtaining confidence intervals is the real gold behind this course, and I intend to share this knowledge with my work peers. In summary, I would focus on this, wouldn't try to reach some "data analysis" and prefer to stick with "Bayesian inference".

This course provides the student a profound understanding of the statistics behind the bayesian approach. Also, it gives some intuition for the difference between the frequentist and the bayesian approach, although that part could have been more explicit in my opinion. It would be very helpful to have more examples on the differences in using freq vs bayesian approach, the gains from using bayesian approach, examples of where the freq approach is limiting / misleading in its 'objectiveness'. More 'real life' examples instead of coin flipping examples - although easy to follow - would be very helpful as well, maybe in a consecutive course with applied bayesian statistics?

An interesting introduction to Bayesian statistics and inference. Not for people with no statistical background, as it does assume you are comfortable with various distributions, expectations, variances, etc. and the 'standard' frequentist worldview (including inferential procedures such as linear regression). The material was well explained, and generally well examined, with a mixture of multiple choice understanding questions, and numeric response tasks which also serve as a very basic introduction to R (or Excel if you are crazy). It was good to see the instructor realising that a light shirt was causing problems and switching to a darker one as the videos went on!

I enjoyed the course. My background is mathematics, but not specifically statistics, though I do have a basic understanding of elementary frequentist statistics. My goal was to understand the fundamentals and uses of Bayesian statistics, having attempted that via a couple of textbooks without much success; this time, I got it!

I do have some reservations about the course. Herbie Lee spent a huge amount of time deriving formulas and methods - a few gaps (either hand waving or 'leave it to the student to finish') would have been helpful, I think. This would leave more time for examples and applications. But the course was well worth my time and effort.

This course covers most of the basics in a very good manner. I personally feel, the last week chapters especially regression do not connect the dots between the foundation that was laid and the resources provided were also not very helpful to fill that gap. For e.g I wanted to understand regression from the bayesian context, the session mostly focused on how to do regression in R and the not the internals of how to understand the mechanics behind from the bayesian stand. I will be helpful to introduce some content that helps the user to move from univariate normal distribution to multivariate normal distribution and explains some intuition behind them.

The course itself is wonderful, and the contents are very thoughtfully selected. I'm not a particular fan of the mirror-technique they use to shoot the videos. Basically, Professor Lee stands in front of a mirror and writes onto the mirror with text markers. On the video you see both him, and the text he writes.

His body often covers the text and generally, it is hard to read. Personally, I see no need to see the professor. Rather, I would prefer a note-taking app (white background). There, old formulas could also be replaced by LaTeX text making everything much more readable, plus there would be downloadable lecture slides automatically.

I liked that the teacher put things into perspective and showed the connections between the different concepts. I deduct 1 star, because the additional material in rare: Meaning, you have to take notes in the lectures to solve the quizzes and to have something for looking things up. Furthermore, in a few lectures it was difficult to read what the teacher was writing, because he was wearing a shirt with a too bright color. (Sounds funny, but I mean this serious ;-) ) In summary, a great lecture and perfect introduction into the concepts. The quizzes are constructed in a way, that they encourage learning rather than frustration.

The course is nice, the lectures are really clear. Professor Lee is brilliant and he often gives some excellent interpretations of Bayesian results. For example, the classic example of testing for rare diseases is explained in terms of ratio of true positives to all positives. Another example is the explanation of predictive mean for normal models, or the explanation of noninformative priors. They're all clearer than what usually found in many books. The only limit of the course is that it's strictly an introduction, thus very useful topics for applications such as hierarchical models or nonconjugate models are not covered.

It was a very nice course that got more practical towards the end. The only thing I found a little bit confusing is the regression part, without theory videos and with practical outcomes that are exactly the same as frequentist approaches.

Don't be discouraged if you come from a background where integers and derivatives are not usual! I come from psychology and I found it a little bit hard at the beginning, but if you put effort you will get to understand almost everything. As long as you get the idea of where things like formulas are coming from and why are they done that way I think it is enough.

I really liked this course. The material is great and the structure of the course is very well organised. A possible improvement, in my opinion, would be to include more explanatory material or take more time in the videos explaining some concepts or derivations. This is why I have to search for other resources in order to grasp some concepts and I took a lot of time in order to completely grasp all the concepts in this course (roughly 10hours for each week). The last week seems a bit rushed and lacks a bit of explanation in the linear regression, non informative priors and in the normal model.

Good curriculum overall, the course can be difficult for students that don't have a strong background of statistics. I found the video lectures lacking because it was mostly formulas and not much explaining. For intuition I had to consult external sources. Most of the quizzes were well designed and challenged our understanding of the subject. While I don't feel that confident in the subject i did gain a good understanding of the overall idea behing bayesian inference.

My advice would be to provide additional videos that give more insight and intuition behind thess concepts.

The information within the course was delivered in a very clear manner and is useful. However, the quizzes left me a bit disappointed: most of the questions are structured in a "follow along" way where most of the information is described within the question and you just have to make a singular observation. While it might be fine for some intra-week quizzes, I definitely expected more from the "Honours" ones. When I join a Coursera specialization, I'm coming not just for the information, but also for a challenge which will help to digest and live through that information.

Really enjoyed weeks 1-3 of the course. It was well done and I felt like I had a good grasp of the materials, and the tests reflected that. The lecturer gave good intuition of what was going on with the math. Week 4 on the other hand was a bit hectic. I didn't feel like I had a good grasp of the material or the underlying math, and lots of it was rushed through. I also didn't feel like the quizzes in week 4 helped me understand the material more. I was able to complete them correctly just by using R, with little understanding of what's going on behind the scenes.

Most of the stuff is explained quite well and I managed to understand it. I am quite satisfied overall and I am glad I completed the course. The exercises, however, were somewhat boring. I wish there were some optional exercises that are more challenging and require you to solve more realistic problems. I also wish there were more additional materials with more in depth theory and examples of how they use these concepts for solving problems that are actually of some use. I feel like these improvements would make the course much more interesting and engaging.

I liked the course. It was a lot of work. It was probably beyond my skill levels. A few more plug in the value examples would have been a great benefit to me. I did not have an recent experience in Calculus and statistics, and I had never used R. However, I got through the course with the chance to retake the quizzes multiple times. That was a good learning experience for me because I could start to figure out how to fill in the blanks, so to speak. I think some more information on course prerequisites would have been helpful.

It was my first Bayesian course. Good introduction! However more accent should be placed on intuitive understanding rather than mathematical formalism. To be fair that the issue not only with this course, that the issue with 90% of all stat courses/books. As for me, I find mathematical formalism is hard to digest, intuitive understanding should come first ... May be it's just because of my limited knowledge of stats. I'll update my belief once I get better understanding of stats:) Thank you very much Dr Lee!

I would highly recommend this course to everyone, who wishes to learn basics of Bayesian statistics. I very much appreciate quizzes, videos and reading material. Few things I recommend to improve: Provide reading material for the theory presented in videos, it would be helpful to have this when I will come back to material later. Also for some quizzes and questions in videos (W2 and W4) latex didn't interpret correctly, so I had to do it on my own by copying it to latex interpreter, which was irritating.

The course helped me get started with Bayesian stats. This course is good if you have seem probability and stats (distributions, pdf, cdf etc.) and want to learn about the Bayesian interpretation. The course picks up pace from 3rd week and the final week seem a bit rushed. I thing more examples of explicit frequentist vs. bayesian interpretation will benefit the learners. Also, 4th week could really use some additional explanatory content. Thanks for this course, I learned something fun and useful! :)

The course is very well explained, one can learn a lot. Although, I missed more texts to guide throughout the classes. I acknowledge the option to make annotation and access the transcript of the classes was very useful, but even so I would like more material, even to summarize some of the most important content of the classes and expressions developed. Apart from that, I felt very satisfied with the course and look forward to learn more and more about Bayesian Statistics!

It is an excellent introductory course, clear and logical. However, there is some room for improvemnt.

Math could have been more detailed. (However, it is not a problem here since the material is easy - one can complete the missing derivations based on hints given in the lectures.)

Lecture notes would be welcome.

I would also like a better justification for using Bayesian methods: examples of practical applications where the methodology brings proven gains.

While the concepts are pretty advanced and worthwhile to go through, I feel like there could have been more videos explaining the concepts behind the math a bit more. It would really help solidify the concepts for people who are rusty or haven't seen statistics/probability in a long time. However, this course definitely has some GREAT practice exercises (and the honors quizzes are so worth it, so DO THEM!). Overall, tremendous effort. Would recommend.

I learned and revised a lot of knowledge that I forgot/did not know before. Yet, the lecture videos were not well-adopted to explain what the equations really mean. The major issue is that the professor is rushing in his explanations. Perhaps, one needs to consider the negative correlation between the length of a video and the quality of the material it can capture.

Anyways, great lecture series and advanced my knowledge. Thank you!

This was a great introduction to bayesian statistic. I have background in Computer Science and Engineering but I have not yet been introduced to Bayesian Statistics. The Quizzes were where the learning was happening for me. Personally I learn the best when I code things up. I wish they had also included coding examples in Python (which is what I used for the quizzes) since that is one on the most popular languages for data science.

The course is in general well structured. The professor used a lot of mathematical equations to explain the contents. I have no problem understanding them. Everything goes smoothly, until the last section: Bayesian Linear Regression (BLE). In the last section, the professor skipped all the mathematics aspects and rushed the content with R / Excel examples. This is not what I expected. Overall, I will rate the course 4 stars.